def crossValidate(data, labels, chunks, dataCont=None):
''' Perform n-fold cross validation.
Given data array, labels, the chunks as folds, the function performs
cross validation by using 4 out of 5 folds as training and the fifth as
testing set. This is repeated 5 times with a different chunk of the fold
serving as testing set. With each fold, both NB and logistic regression
is fitted. The validation error with both algorithms are returned.
'''
errLG = np.empty(len(chunks)) # pred error, logistic regression
nIters = np.empty(len(chunks),
int) # number of iterations for logistic reg
errNB = np.empty(len(chunks)) # pred error, naive Bayes
for ck in range(len(chunks)):
# get index and dataset for current fold of cross-validation
trnIdx = np.hstack([x for n, x in enumerate(chunks) if n != ck])
vldIdx = np.hstack([x for n, x in enumerate(chunks) if n == ck])
dataTrain, labelTrain = data[trnIdx], labels[trnIdx] # training
dataTest, labelTest = data[vldIdx], labels[vldIdx] # validation
if labels.ndim > 1: # if labels have more than one dimension
labelTest = labelTest.argmax(axis=1) # collapse to 1D
## Fit and predict with naive Bayes
prb = NB.NB_Train(dataTrain, labelTrain)
predNB = NB.NB_Pred(dataTest, prb)
errNB[ck] = errRate(predNB, labelTest) # error with naive Bayes
## Fit and predict with logistic regression
if dataCont is not None: # if given non-discretized data
dataTrain = dataCont[trnIdx]
dataTest = dataCont[vldIdx]
if labels.ndim > 1: # if more than two classes, use multinomial logistic
wts, nIters[ck] = LG.fitLogisticNK(dataTrain, labelTrain, 0.5)
predLG = LG.predLogisticNK(dataTest, wts)
else: # binary response var, use regular logistic regression
wt, nIters[ck] = LG.fitLogisticReg(dataTrain, labelTrain, 0.5)
predLG = LG.predLogistic(dataTest, wt)
errLG[ck] = errRate(predLG, labelTest) # error with logistic reg
return errLG, errNB, nIters
